Fix inpaint node to new manager

invokeai/app/invocations/generate.py

@@ -12,127 +12,68 @@
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 from invokeai.backend.generator.inpaint import infill_methods
 
-from ...backend.generator import Img2Img, Inpaint, InvokeAIGenerator, Txt2Img
+from ...backend.generator import Inpaint, InvokeAIGenerator
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ..util.step_callback import stable_diffusion_step_callback
 from .baseinvocation import BaseInvocation, InvocationConfig, InvocationContext
 from .image import ImageOutput
 
+import re
+from ...backend.model_management.lora import ModelPatcher
+from ...backend.stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
+from .model import UNetField, VaeField
+from .compel import ConditioningField
+from contextlib import contextmanager, ExitStack, ContextDecorator
+
 SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
 INFILL_METHODS = Literal[tuple(infill_methods())]
 DEFAULT_INFILL_METHOD = (
     "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
 )
 
 
-class SDImageInvocation(BaseModel):
-    """Helper class to provide all Stable Diffusion raster image invocations with additional config"""
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["stable-diffusion", "image"],
-                "type_hints": {
-                    "model": "model",
-                },
-            },
-        }
-
-
-# Text to image
-class TextToImageInvocation(BaseInvocation, SDImageInvocation):
-    """Generates an image using text2img."""
-
-    type: Literal["txt2img"] = "txt2img"
-
-    # Inputs
-    # TODO: consider making prompt optional to enable providing prompt through a link
-    # fmt: off
-    prompt: Optional[str] = Field(description="The prompt to generate an image from")
-    seed:        int = Field(ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed)
-    steps:       int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
-    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
-    height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
-    cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
-    model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
-    control_model: Optional[str] = Field(default=None, description="The control model to use")
-    control_image: Optional[ImageField] = Field(default=None, description="The processed control image")
-    # fmt: on
+from .latent import get_scheduler
 
-    # TODO: pass this an emitter method or something? or a session for dispatching?
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
+class OldModelContext(ContextDecorator):
+    model: StableDiffusionGeneratorPipeline
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Handle invalid model parameter
-        model = context.services.model_manager.get_model(self.model,node=self,context=context)
+    def __init__(self, model):
+        self.model = model
 
-        # loading controlnet image (currently requires pre-processed image)
-        control_image = (
-            None if self.control_image is None
-            else context.services.images.get_pil_image(self.control_image.image_name)
-        )
-        # loading controlnet model
-        if (self.control_model is None or self.control_model==''):
-            control_model = None
-        else:
-            # FIXME: change this to dropdown menu?
-            # FIXME: generalize so don't have to hardcode torch_dtype and device
-            control_model = ControlNetModel.from_pretrained(self.control_model,
-                                                            torch_dtype=torch.float16).to("cuda")
+    def __enter__(self):
+        return self.model
 
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+    def __exit__(self, *exc):
+        return False
 
-        txt2img = Txt2Img(model, control_model=control_model)
-        outputs = txt2img.generate(
-            prompt=self.prompt,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            control_image=control_image,
-            **self.dict(
-                exclude={"prompt", "control_image" }
-            ),  # Shorthand for passing all of the parameters above manually
-        )
-        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
-        # each time it is called. We only need the first one.
-        generate_output = next(outputs)
+class OldModelInfo:
+    name: str
+    hash: str
+    context: OldModelContext
 
-        image_dto = context.services.images.create(
-            image=generate_output.image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
+    def __init__(self, name: str, hash: str, model: StableDiffusionGeneratorPipeline):
+        self.name = name
+        self.hash = hash
+        self.context = OldModelContext(
+            model=model,
         )
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
 
+class InpaintInvocation(BaseInvocation):
+    """Generates an image using inpaint."""
 
-class ImageToImageInvocation(TextToImageInvocation):
-    """Generates an image using img2img."""
+    type: Literal["inpaint"] = "inpaint"
 
-    type: Literal["img2img"] = "img2img"
+    positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
+    negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
+    seed:        int = Field(ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed)
+    steps:       int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
+    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
+    height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
+    cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
+    unet: UNetField = Field(default=None, description="UNet model")
+    vae: VaeField = Field(default=None, description="Vae model")
 
     # Inputs
     image: Union[ImageField, None] = Field(description="The input image")
@@ -144,72 +85,6 @@ class ImageToImageInvocation(TextToImageInvocation):
         description="Whether or not the result should be fit to the aspect ratio of the input image",
     )
 
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = (
-            None
-            if self.image is None
-            else context.services.images.get_pil_image(self.image.image_name)
-        )
-
-        if self.fit:
-            image = image.resize((self.width, self.height))
-
-        # Handle invalid model parameter
-        model = context.services.model_manager.get_model(self.model,node=self,context=context)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-        outputs = Img2Img(model).generate(
-            prompt=self.prompt,
-            init_image=image,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
-        )
-
-        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
-        # each time it is called. We only need the first one.
-        generator_output = next(outputs)
-
-        image_dto = context.services.images.create(
-            image=generator_output.image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-class InpaintInvocation(ImageToImageInvocation):
-    """Generates an image using inpaint."""
-
-    type: Literal["inpaint"] = "inpaint"
-
     # Inputs
     mask: Union[ImageField, None] = Field(description="The mask")
     seam_size: int = Field(default=96, ge=1, description="The seam inpaint size (px)")
@@ -252,6 +127,14 @@ class InpaintInvocation(ImageToImageInvocation):
         description="The amount by which to replace masked areas with latent noise",
     )
 
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["stable-diffusion", "image"],
+            },
+        }
+
     def dispatch_progress(
         self,
         context: InvocationContext,
@@ -265,6 +148,49 @@ def dispatch_progress(
             source_node_id=source_node_id,
         )
 
+    def get_conditioning(self, context):
+        c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
+
+        return (uc, c, extra_conditioning_info)
+
+    @contextmanager
+    def load_model_old_way(self, context, scheduler):
+        unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
+        vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
+
+        #unet = unet_info.context.model
+        #vae = vae_info.context.model
+
+        with ExitStack() as stack:
+            loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
+
+            with vae_info as vae,\
+                 unet_info as unet,\
+                 ModelPatcher.apply_lora_unet(unet, loras):
+
+                device = context.services.model_manager.mgr.cache.execution_device
+                dtype = context.services.model_manager.mgr.cache.precision
+
+                pipeline = StableDiffusionGeneratorPipeline(
+                    vae=vae,
+                    text_encoder=None,
+                    tokenizer=None,
+                    unet=unet,
+                    scheduler=scheduler,
+                    safety_checker=None,
+                    feature_extractor=None,
+                    requires_safety_checker=False,
+                    precision="float16" if dtype == torch.float16 else "float32",
+                    execution_device=device,
+                )
+
+                yield OldModelInfo(
+                    name=self.unet.unet.model_name,
+                    hash="<NO-HASH>",
+                    model=pipeline,
+                )
+
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
             None
@@ -277,24 +203,30 @@ def invoke(self, context: InvocationContext) -> ImageOutput:
             else context.services.images.get_pil_image(self.mask.image_name)
         )
 
-        # Handle invalid model parameter
-        model = context.services.model_manager.get_model(self.model,node=self,context=context)
-
         # Get the source node id (we are invoking the prepared node)
         graph_execution_state = context.services.graph_execution_manager.get(
             context.graph_execution_state_id
         )
         source_node_id = graph_execution_state.prepared_source_mapping[self.id]
 
-        outputs = Inpaint(model).generate(
-            prompt=self.prompt,
-            init_image=image,
-            mask_image=mask,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
-        )
+        conditioning = self.get_conditioning(context)
+        scheduler = get_scheduler(
+            context=context,
+            scheduler_info=self.unet.scheduler,
+            scheduler_name=self.scheduler,
+        )
+
+        with self.load_model_old_way(context, scheduler) as model:
+            outputs = Inpaint(model).generate(
+                conditioning=conditioning,
+                scheduler=scheduler,
+                init_image=image,
+                mask_image=mask,
+                step_callback=partial(self.dispatch_progress, context, source_node_id),
+                **self.dict(
+                    exclude={"positive_conditioning", "negative_conditioning", "scheduler", "image", "mask"}
+                ),  # Shorthand for passing all of the parameters above manually
+            )
 
         # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
         # each time it is called. We only need the first one.

invokeai/backend/__init__.py

@@ -5,7 +5,6 @@
     InvokeAIGeneratorBasicParams,
     InvokeAIGenerator,
     InvokeAIGeneratorOutput,
-    Txt2Img,
     Img2Img,
     Inpaint
 )

invokeai/backend/generator/__init__.py

@@ -5,7 +5,6 @@
     InvokeAIGenerator,
     InvokeAIGeneratorBasicParams,
     InvokeAIGeneratorOutput,
-    Txt2Img,
     Img2Img,
     Inpaint,
     Generator,